Bottom Line:
Mixed-effects maximum likelihood regression and an ensemble of conditional inference trees were used to analyse data.Although reliable cost drivers were identified, idiosyncrasies of mental health care complicated the identification of clear and consistent differences in hospital costs between patient groups.Further research could greatly inform current discussions about inpatient mental health reimbursement, in particular with multicentre studies that might find algorithms to split patients in more resource-homogeneous groups.

Introduction: An understanding of differences in hospital costs between patient groups is relevant for the efficient organisation of inpatient care. The main aim of this study was to confirm the hypothesis that eight a priori identified cost drivers influence per diem hospital costs. A second aim was to explore further variables that might influence hospital costs.

Methods: The study included 667 inpatient episodes consecutively discharged in 2014 at the psychiatric hospital of the Medical Centre-University of Freiburg. Fifty-one patient characteristics were analysed. Per diem costs were calculated from the hospital perspective based on a detailed documentation of resource use. Mixed-effects maximum likelihood regression and an ensemble of conditional inference trees were used to analyse data.

Results: The study confirmed the a priori hypothesis that not being of middle age (33-64 years), danger to self, involuntary admission, problems in the activities of daily living, the presence of delusional symptoms, the presence of affective symptoms, short length of stay and the discharging ward affect per diem hospital costs. A patient classification system for prospective per diem payment was suggested with the highest per diem hospital costs in episodes having both delusional symptoms and involuntary admissions and the lowest hospital costs in episodes having neither delusional symptoms nor somatic comorbidities.

Conclusion: Although reliable cost drivers were identified, idiosyncrasies of mental health care complicated the identification of clear and consistent differences in hospital costs between patient groups. Further research could greatly inform current discussions about inpatient mental health reimbursement, in particular with multicentre studies that might find algorithms to split patients in more resource-homogeneous groups.

Mentions:
Fig 4 shows the single best conditional inference tree in the analysed data. The best cost split in the complete data set was found in the presence of delusional symptoms, followed by somatic comorbidities in patients without delusional symptoms and involuntary admission in patients with delusional symptoms. The tree-building procedure was configured to stop when the Bonferroni-adjusted tests of the global hypothesis of no association between any of the covariates and the outcome could not be rejected at the 95% confidence level. The bold terminal nodes represent potential groups for payment purpose. The highest hospital costs were found in episodes with both delusional symptoms and involuntary admission. The lowest hospital costs were found in episodes with neither delusional symptoms, nor somatic comorbidities. The explained variance was 14% and the RMSE was 62.

Mentions:
Fig 4 shows the single best conditional inference tree in the analysed data. The best cost split in the complete data set was found in the presence of delusional symptoms, followed by somatic comorbidities in patients without delusional symptoms and involuntary admission in patients with delusional symptoms. The tree-building procedure was configured to stop when the Bonferroni-adjusted tests of the global hypothesis of no association between any of the covariates and the outcome could not be rejected at the 95% confidence level. The bold terminal nodes represent potential groups for payment purpose. The highest hospital costs were found in episodes with both delusional symptoms and involuntary admission. The lowest hospital costs were found in episodes with neither delusional symptoms, nor somatic comorbidities. The explained variance was 14% and the RMSE was 62.

Bottom Line:
Mixed-effects maximum likelihood regression and an ensemble of conditional inference trees were used to analyse data.Although reliable cost drivers were identified, idiosyncrasies of mental health care complicated the identification of clear and consistent differences in hospital costs between patient groups.Further research could greatly inform current discussions about inpatient mental health reimbursement, in particular with multicentre studies that might find algorithms to split patients in more resource-homogeneous groups.

Introduction: An understanding of differences in hospital costs between patient groups is relevant for the efficient organisation of inpatient care. The main aim of this study was to confirm the hypothesis that eight a priori identified cost drivers influence per diem hospital costs. A second aim was to explore further variables that might influence hospital costs.

Methods: The study included 667 inpatient episodes consecutively discharged in 2014 at the psychiatric hospital of the Medical Centre-University of Freiburg. Fifty-one patient characteristics were analysed. Per diem costs were calculated from the hospital perspective based on a detailed documentation of resource use. Mixed-effects maximum likelihood regression and an ensemble of conditional inference trees were used to analyse data.

Results: The study confirmed the a priori hypothesis that not being of middle age (33-64 years), danger to self, involuntary admission, problems in the activities of daily living, the presence of delusional symptoms, the presence of affective symptoms, short length of stay and the discharging ward affect per diem hospital costs. A patient classification system for prospective per diem payment was suggested with the highest per diem hospital costs in episodes having both delusional symptoms and involuntary admissions and the lowest hospital costs in episodes having neither delusional symptoms nor somatic comorbidities.

Conclusion: Although reliable cost drivers were identified, idiosyncrasies of mental health care complicated the identification of clear and consistent differences in hospital costs between patient groups. Further research could greatly inform current discussions about inpatient mental health reimbursement, in particular with multicentre studies that might find algorithms to split patients in more resource-homogeneous groups.